Machine Learning Algorithms Used to Predict Atrocities

Statistical models that use socio-political data to predict mass atrocities could soon inform governments and NGOs on how and where to take preventative action. The models emerged from one segment of the Tech Challenge for Atrocity Prevention, a competition run by the US Agency for International Development (USAID) and NGO Humanity International. The winners were announced last November and will now work with the organizer to further develop their technology solutions.

Approximately 1.5 billion people live in countries affected by conflict, sometimes including atrocities such as genocides, mass rape and ethnic cleansing, according to the World Bank’s World Development Report 2011. Many of these countries are in the developing world.

Five winners from different countries won between US$1,000 and US$12,000 and were among nearly 100 entrants who developed algorithms to predict when and where mass atrocities are likely to happen. The competition organizers hope the new algorithms could help governments and human rights organizations identify at-risk regions, potentially allowing them to intervene before mass atrocities happen.

The competition started from the premise that certain social and political measurements are linked to increased likelihood of atrocities. Yet because such factors interact in complex ways, organizations working to prevent atrocities lack a reliable method of predicting when and where they might happen next.

The algorithms use sociopolitical indicators and data on past atrocities as training sets. The data were drawn from archives such as the Global Database of Events, Language and Tone, a data set that encodes more than 200 million globally newsworthy events, recording cultural information such as the people involved, their location and any religious connections.

A participant associated with one of the entries that won an “ideation” award made this observation:

Algorithms are vital to the prediction and prevention of atrocities as computers have the unique capability of seeing patterns in large data sets that humans often miss,” says David Mace of the California Institute of Technology.

Mace’s algorithm – as yet only at the design stage – views the world as a mosaic of harmful and neutral regions tenuously interacting with each other. It models the flow of tension between countries to predict where relations are stressed and violence is likely to occur. The challenge also asked entrants to develop new ways of collecting social data from hard-to-access locations.

With all sub-challenges now complete, USAID and Humanity United will work with the winners to develop and pilot new tools for atrocity prevention.

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